This invention relates generally to neuromorphic synapses and, more specifically to, neuromorphic synapses based on resistive memory cells.
Neuromorphic technology relates to computing systems which are inspired by biological architectures of the nervous system. Conventional computing architectures are becoming increasingly inadequate to meet the ever-expanding processing demands placed on modern computer systems. Compared to the human brain, the classical von Neumann computer architecture is highly inefficient in terms of power consumption and space requirements. The human brain occupies less than 2 liters and consumes around 20 W of power. Simulating 5 seconds of brain activity using state-of-the-art supercomputers takes around 500 s and needs 1.4 MW of power. These issues have prompted a significant research effort to understand the highly efficient computational paradigm of the human brain and to create artificial cognitive systems with unprecedented computing power.
Neurons and synapses are two basic computational units in the brain. A neuron can integrate inputs coming from other neurons, in some cases with further inputs, for example from sensory receptors, and generates output signals known as “action potentials” or “spikes”. The synapses change their connection strength as a result of neuronal activity. FIG. 1 of the accompanying drawings shows a schematic representation of a synapse 1 located between two neurons 2. The synapse 1 receives action potentials generated by a pre-synaptic neuron (“pre-neuron”) N1 and provides output signals to a post-synaptic neuron (“post-neuron”) N2. The pre-neuron action potential is conveyed to synapse 1 via an axon 3 of neuron N1. The resulting synaptic output signal is a graded synaptic potential which depends on conductance (also known as “synaptic weight” or “strength”) of the synapse. Synaptic weight can be enhanced or reduced by neuronal activity, and this “plasticity” of synapses is crucial to memory and other brain functions. This effect is indicated in FIG. 1 by back-propagation of the post-neuron action potential, i.e. a spike generated by neuron N2, to the synapse 1 via a dendrite 4 of neuron N2.
The action potentials in biological systems have the same shape at all instances of neuronal firing (spike generation). There is no information in the spike shape but only in the firing time. In particular, synaptic weight can be modified in dependence on relative timing of the pre-neuron and post-neuron action potentials. In a simple model here, synapses become increasingly stronger (more conductive) if the pre- and post-neurons fire together. Change in synaptic weight may also depend on slight differences in timing of the pre-and post-neuron spikes. For example, synaptic weight may increase if the post-neuron tends to fire just after the pre-neuron, and decrease if the post-neuron tends to fire just before the pre-neuron. These relative timing effects are known generally as spike-timing dependent plasticity (STDP).
Synapses typically outnumber neurons by a significant factor (approximately 10,000 in the case of the human brain). A key challenge in neuromorphic computation technology is the development of compact nanoelectronic devices that emulate the plasticity of biological synapses.